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Citi Logik and the future for Mobile Network Data (MND) in transport


Future of Mobile Network Data and transport

Andy Graham is an independent traffic consultant who specialises in getting new data into mainstream use.


He has worked on a wide range of projects, including traffic modelling, predicting traffic patterns, connected vehicles, parking projects and special events taking place in cities where parking is at a premium.


He now focuses on assembling a range of data sets to inform transport projects and believes that Mobile Network Data (MND) will continue to play a key role, alongside other data.


“The use of MND has come on leaps and bounds in the past few years and has become one of the key data sets for transport,” he said. “Previously, people interviewed drivers by the side of the road. Once you start using anonymised MND, you get origin-destination and importantly the path people are taking. MND is great on giving a broader view and greater scale than traditional methods.”


The use of MND has come on leaps and bounds in the past few years and has become one of the key data sets for transport


The changing world and mobile network data


In Andy’s view, changing behaviour makes MND more relevant than ever in transport planning and operations.


“Things are changing because the pandemic has had long-term implications on people’s behaviour. Where people are coming from and going to isn’t the same as it was. If you own or operate infrastructure, a business or service, whether it’s a local authority, an airfield or a shopping centre, you need to understand new patterns. Data on travel patterns will be key to siting Electric Vehicle charge points too, so they are in places where people need to charge.”


The availability of data is on the up


Andy believes that ability to interpret the raw data is critical. “Transport planners were data-poor and processing-rich, they had one or two pieces of data and computers to process that data with. Now they are data-rich, and it can be hard to see the wood for the trees or to reconcile conflicting data from different sources. That is where artificial intelligence and service providers like Citi Logik can help. Getting the data is easy, the trick is to understand what it all means.”


He predicts that mobile devices will continue to be a key source of data for transport, despite the potential for autonomous technology in vehicles. “I hear a lot about automated vehicles,” he said, “but we have about 60m phones in the UK and 33m vehicles. Many of these are connected by a mobile device, be it via a smartphone, in-vehicle GPS device for insurance, or simply a sat nav. All these give mobile network data. It will take a long time to get automated vehicles up to the current penetration of mobile devices. Also, by using a mobile device we get data on different vehicle types moving e.g. buses and cycles, trucks etc. at different granularities via a mix of MND and GPS. No single source is the unique answer – and we are not reliant on data from a single-vehicle manufacturer, as if you make decisions on the basis of that, you will end up with the wrong answer.”


The future of MND


He highlighted that MND’s application can be broadened by combining it with other data. “MND can be blended with other data such as from GPS, and image processing. If you have a camera at a junction, you can identify buses and vehicles and the spacing of pedestrians. Then if you use data like MND you can also work out where this traffic is going to and from. You can get a wide and very accurate journey time sample with GPS. Each approach has its strengths and weaknesses.”


MND can be blended with other data such as from GPS, and image processing. If you have a camera at a junction, you can identify buses and vehicles and the spacing of pedestrians. Then if you use data like MND you can also work out where this traffic is going to and from. You can get a wide and very accurate journey time sample with GPS.


Long term values


Last, but not least, another benefit of data from mobile devices in his view was that it was long term good value. “Compared to the cost of building and more importantly maintenance of physical infrastructure, buying data as a service is often a cost-effective option long term. I’ve seen cases where people monitor traffic with physical equipment by the side of the road but they get vandalised or have power failures and can’t maintain them anymore – that is very last century and not a good investment.”


Compared to the cost of building and more importantly maintenance of physical infrastructure, buying data as a service is often a cost-effective option long term.

More information about Citi Logik’s transport data capabilities can be found here.


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